How AI-Augmented Training ImprovesWorker Productivity (R&R AEJ: Applied Economics)
with Didier Fouarge Marie-Christine Fregin, Mark Levels, Raymond Montizaan, Pelin Özgül, Nicholas Rounding and Michael Stops (Link to Working Paper)
We analyze the impact of AI-augmented training on worker productivity in a financial services company. The company introduced an AI tool that provides performance feedback on call center agents to guide their training. To estimate causal effects, we exploit the staggered roll out of the AI-tool. The AI-augmented training reduces call handling time by 10 percent. We find larger effects for short-tenured workers because they spend less time putting clients on hold. But the AI-augmented training also improves communication style with relatively stronger effects for long-tenured agents, and we find slightly positive effects on customer satisfaction.
Adjusters and Casualties: The Anatomy of Labor Market Displacement (Under Review)
with Eric Hanushek, Jacob Light and Lisa Simon (Link to Working Paper)
We analyze the full distribution of displaced workers’ earnings losses using a new method that combines matching and synthetic control group approaches at the individual level. We find that the distribution of earnings losses is highly skewed. Average losses, as estimated by conventional event studies, are driven by a small number of workers who suffer catastrophic losses, while most recover quickly. Observable worker characteristics explain only a small fraction of the variance in earnings losses. Instead, we find substantial heterogeneity in earnings losses even among workers displaced by the same firm who have identical observed characteristics such as education, age, and gender. Workers with minimal earnings losses adjust quickly by switching industries, occupations, and especially regions, while comparable workers with catastrophic losses adjust slowly, even though they are forced to make comparable numbers of switches in the long run.